1,721,027 research outputs found
Shop floor management using a fuzzy inference controller
No full textThis paper proposes a fuzzy model which ranks jobs according to a linear combination of different priorities whose weights reflect the current status of the system. The priorities are determined applying a number of fuzzy rules having as premises attributes related to the features of each job. The weights, instead, are determined according to fuzzy rules having as premises attributes related to the current status of the system. This fuzzy model provides a methodological basis for developing shop floor management systems. Shop floor management systems are concerned with decisions regarding part scheduling and resource allocation
Modelling and optimization of industrial manufacturing processes subject to no-wait constraints
No full text
Reducing the physical ergonomic risk by job rotation: A simulation-based approach
Full text linkSince years, researchers have proposed job rotation as a solution to balance physical workload and reduce ergonomic risk among operators. Simulation is a strong tool for rapid ergonomic analysis, which allows testing different configurations and evaluating the characteristics of different operators. Thanks to such tool, this study proposes a new approach to improve a real system and reduce the ergonomic risk among operators. A real assembly station has been involved in the study and a postural analysis has been performed considering real variables of the system. Different potential workers with different anthropometric characteristics have been tested. The critical situations have been identified and solved proposing different job rotation solutions. Simulation results confirm that job rotation is a good approach to reduce the ergonomic risk and they provide practical guidelines for task assignment
Miglioramento di sicurezza e manutenibilità di macchine operatrici. Proposta di un modello per la verifica di conformità e performance
No full textMaintenance management for geographically distributed assets: a criticality-based approach
No full textThis paper provides a model to help decision-makers to choose the daily maintenance strategy for geographically distributed assets (GDA) where sites are located in a wide geographical area and a single maintenance centre is involved in managing the maintenance. A hierarchical structure has been used to represent the Multi-System Multi-Component network (MSMCN). A quantitative framework with sequential steps has been developed to plan a daily mix of maintenance actions. First, a dynamic criticality analysis identifies the critical items. A second screening adopts reliability thresholds to determine components that could be preventively replaced. Finally, an iterative economic comparison procedure selects the activities to schedule day by day. The proposed approach also considers time and resources constraints. The model was applied to a real case study to verify its feasibility. Results were compared to the results obtained implementing the current strategy in terms of total downtime, total number of sites visited and total maintenance cost. It was demonstrated that it is possible to reduce the total maintenance cost and the total number of sites visited in a year by balancing opportunistic and preventive maintenance activities with an appropriate selection of the model's thresholds
A new perspective for production process analysis using additive manufacturing - complexity vs production volume
Full text linkSince the early days of industrial manufacturing, decisions like the analysis of economic or financial break even and the determination of optimal production level and similar problems were taken using the number of parts as the main decision driver. While in the last years the manufacturing community was simply observing the evolutions of the additive manufacturing (AM) technology, nowadays it is changing its perspective to be a player of this revolution. Since AM has become feasible and applicable to the industrial manufacturing, as reported in Fera et al. (Cogent Eng 3(1):1261503, 2016) and Costabile et al. (Int J Ind Eng Comput 8(2):263â282, 2017), the industrial world is now calling for innovative methods for the integration of this new technology operations management in traditional production systems. The aim of this paper is to present a novel approach to classify and analyze the production of specific products using AM or subtractive manufacturing (SM), using complexity as a decision driver and not the number of products to manufacture. The effectiveness of the method is tested on a data set built for this scope
A Modified genetic Algorithm for Time and Cost Optimization of an Additive Manufacturing Single-Machine Scheduling
No full textAdditive Manufacturing (AM) is a process of joining materials to make objects from 3D model
data, usually layer by layer, as opposed to subtractive manufacturing methodologies. Selective
Laser Melting, commercially known as Direct Metal Laser Sintering (DMLS®), is the most
diffused additive process in today’s manufacturing industry. Introduction of a DMLS® machine
in a production department has remarkable effects not only on industrial design but also on
production planning, for example, on machine scheduling. Scheduling for a traditional single
machine can employ consolidated models. Scheduling of an AM machine presents new issues
because it must consider the capability of producing different geometries, simultaneously. The
aim of this paper is to provide a mathematical model for an AM/SLM machine scheduling. The
complexity of the model is NP-HARD, so possible solutions must be found by metaheuristic
algorithms, e.g., Genetic Algorithms. Genetic Algorithms solve sequential optimization
problems by handling vectors; in the present paper, we must modify them to handle a matrix.
The effectiveness of the proposed algorithms will be tested on a test case formed by a 30 Part
Number production plan with a high variability in complexity, distinct due dates and low
production volume
No-wait lines optimisation: a procedure based on scheduling enhanced by digital twin
No full textThe no-wait scheduling problem involves planning the sequence of tasks in a job without any delay. This constraint is crucial in industries where production interruptions may create significant inefficiencies or quality issues. This paper addresses this problem by proposing a new methodology to design and verify a work-cycle related layout, focusing on both job-shop and flow-shop environments and finally proving its efficiency thanks to the use of a digital twin to validate the production process. The goal of the optimisation is the makespan minimisation and it is achieved through the application of the timetabling heuristic method. The iterative nature of the proposed model allows for a continuous improvement in scheduling, and queue reduction is verified thanks to the digital twin. The iterative procedure is tested on a real galvanic no-wait line of a primary aerospace international company; results show that the methodology application guarantees respect to the no-wait constraint and the minimisation of the production times, which concur to increase the demand satisfaction for the company
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